With the advancement of data acquisition and storage techniques, volume datasets are steadily increasing in both size and resolution. As a result, we need efficient methods to visualize these datasets and cope with bandwidth and storage limitations. Researchers are investigating ways to adapt a common visualization technique, namely isosurface extraction, to the focus of the visualization.
Polygonal approximations of isosurfaces extracted from uniformly sampled volumes are increasing in size due to the availability of higher resolution imaging techniques. The large number of primitives represented hinders the interactive exploration of the dataset. Though many solutions have been proposed to this problem, many require the creation of isosurfaces at multiple resolutions or the use of additional data structures, often hierarchical, to represent the volume.
A typical strategy is to make the extraction adaptive to the local complexity of the isosurface in order to produce meshes that are fine in areas of interest and coarse in the remaining ones. This strategy differs from extracting vertices uniformly over the isosurface. In this way, the overall density of extracted vertices is significantly reduced while preserving the quality of the isosurface. The immediate benefit is the reduction of the cost in storage, transmission and rendering.
Many researchers have sought to extend common isosurface extraction methods, such as the well-known Lorenson's “Marching Cubes” algorithm, to produce adaptively tessellated isosurfaces. A prevalent idea, as is presented by Saupe in the article “Optimal memory constrained isosurface extraction”, for the solution to this problem requires the use of algorithms and data structures that are often very specific to certain visualization scenarios and typically require large amounts of additional storage. Commonly, structures such as octrees and interval trees have been applied to address this problem. Furthermore, to facilitate the transition from finely sampled regions to coarser ones, multi-resolution hierarchies are used. These techniques provide results of quality, however, their costs in storage can be a limitation. Several techniques providing feature-driven extraction have been previously implemented, for example, by Wood in the article “Semi-regular Mesh Extraction From Volume,” and Kobbelt in “Feature-Sensitive Surface Extraction From Volume Data.” Some of these techniques use multi-resolution hierarchies in order to allow users to select areas to be refined adaptively.